We also compare with recent two-hand pose estimation approaches, i.e. InterNet [1]}, Rong et al.  [2]}, and DIGIT [3]}. We show superior performance over these 3D interacting hand pose estimation systems. For example, our approach significantly improves upon Moon et al.  [1]}’s state-of-the-art results from \(14.21\) mm to \(10.97\) mm (about 23% error reduction) on the “IH26M-ALL” split. The clear performance gap validates the effectiveness of our framework. Overall, our approach consistently ranks the first across all evaluation protocols.
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